Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2020 May;99(20):e20340.
doi: 10.1097/MD.0000000000020340.

Identification of crucial genes correlated with esophageal cancer by integrated high-throughput data analysis

Wei Zhou  1 , Jiarui Wu, Xinkui Liu, Mengwei Ni, Ziqi Meng, Shuyu Liu, Shanshan Jia, Jingyuan Zhang, Siyu Guo, Xiaomeng Zhang
Affiliations

Identification of crucial genes correlated with esophageal cancer by integrated high-throughput data analysis

Wei Zhou et al. Medicine (Baltimore). 2020 May.

Abstract

Background: Esophageal cancer (ESCA) is one of the most deadly malignancies in the world. Although the management and treatment of patients with ESCA have improved, the overall 5-year survival rate is still very poor.

Methods: The study aimed to identify potential key genes associated with the pathogenesis and prognosis of ESCA. In the study, integrated bioinformatics methods were used to screen differentially expressed genes (DEGs) between ESCA and normal tissue in the data set of gene expression profiles. The hub gene in DEGs was further analyzed by protein-protein interaction (PPI) network and survival analysis to explore its relationship with the pathogenesis and poor prognosis of ESCA.

Results: 134 up-regulated genes and 183 down-regulated genes were obtained in ESCA compared with normal tissues. Moreover, the PPI network was established with 176 nodes and 800 interactions. Ten hub genes (AURKA, CDC20, BUB1, TOP2A, ASPM, DLGAP5, TPX2, CENPF, UBE2C, and NEK2) were filtered out based on the degree value. Functional enrichment analysis indicated that a variety of extracellular related items and ECM-receptor interaction pathway were all correlated with the ESCA.

Conclusions: The results of this study would provide some guidance for further study of diagnostic and prognostic biomarkers to promote ESCA treatment.

PubMed Disclaimer

Conflict of interest statement

The authors have no conflicts of interest to disclose.

Figures

Figure 1
Figure 1
Workflow for identification of hub genes and pathways for ESCA.
Figure 2
Figure 2
Volcano plot of gene expression profile data in ESCA samples and normal ones and heat map of differentially expressed gene (DEGs). (A) Volcano plot of GSE17351. (B) Volcano plot of GSE20347. (C) Volcano plot of GSE29001. (D) Volcano plot of GSE92396. (E) Volcano plot of GSE100942. (F) Heat map of differentially expressed genes. Green represents a lower expression level, red represents higher expression levels, and white represents that there is no different expression amongst the genes. Each column represents one dataset and each row represents 1 gene. The number in each rectangle represents the normalized gene expression level. The gradual color ranged from green to red represents the changing process from down-regulation to up-regulation.
Figure 3
Figure 3
Graph of the functional enrichment analysis results. (A) KEGG pathway and DEGs enriched in pathways. The yellow triangle represents the KEGG pathways. The red dots are the common DEGs enriched in the 2 pathways, and the pink dots represent the other DEGs enriched in the pathways. (B) Top 20 of pathway GO enrichment. (C) The circular map of the DEGs distribution of the top 5 GO pathway. The red indicates the up-regulated gene and the blue indicates the down-regulated gene. (D) DEGs clustering map of the first 5 GO pathways. In the figure, from the inside to the outside are gene clustering, |log 2FC| and GO pathway.
Figure 4
Figure 4
Graph of the ECM–receptor interaction pathway (17). The orange nodes show the DEGs in the pathway and the green nodes indicate other genes.
Figure 5
Figure 5
PPI network of DEGs in ESCA. Purple represents the hub genes in DEGs, and the depth of the color and size of the nodes are proportional to the degree value. Yellow dot represents the gene which degree value is greater than average and red represents other genes.
Figure 6
Figure 6
Four significant modules identified from the PPI network. (A) Module 1 contained 28 nodes and 355 interactions, MCODE score = 26; (B) module 2 contained 11 nodes and 55 sides, MCODE score = 11; (C) module 3 contained 9 nodes and 36 interactions, MCODE, score = 9; (D) module 4 contained 9 nodes and 36 interactions, MCODE, score = 9.
Figure 7
Figure 7
Prognostic roles of 10 hub genes in the EA patients. Survival curves are plotted for EA cancer patients. (A) ASPM; (B) AURKA; (C) BUB1; (D) CDC20; (E) CENPF; (F) DLGAP5; (G) NEK2; (H) TOP2A; (I) TPX2; and (J) UBE2C.
Figure 8
Figure 8
Prognostic roles of 10 hub genes in the ESCC patients. Survival curves are plotted for ESCC cancer patients. (A) ASPM; (B) AURKA; (C) BUB1; (D) CDC20; (E) CENPF; (F) DLGAP5; (G) NEK2; (H) TOP2A; (I) TPX2; and (J) UBE2C.
Figure 9
Figure 9
Analysis of 10 hub genes expression level in human ESCA. The red and gray boxes represent cancer and normal tissues, respectively. (A) ASPM; (B) AURKA; (C) BUB1; (D) CDC20; (E) CENPF; (F) DLGAP5; (G) NEK2; (H) TOP2A; (I) TPX2; and (J) UBE2C.
Figure 10
Figure 10
Correlation analysis of 9 hub genes and AURKA in ESCA. (A) AURKA; (B) BUB1; (C) CDC20; (D) CENPF; (E) DLGAP5; (F) NEK2; (G) TOP2A; (H) TPX2; and (I) UBE2C.
See this image and copyright information in PMC

References

    1. Bray F, Ferlay J, Soerjomataram I, et al. Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin 2018;68:394–424. - PubMed
    1. Torre LA, Bray F, Siegel RL, et al. Global cancer statistics, 2012. CA Cancer J Clin 2015;65:87–108. - PubMed
    1. Arnold M, Laversanne M, Brown LM, et al. Predicting the future burden of esophageal cancer by histological subtype: international trends in incidence up to 2030. Am J Gastroenterol 2017;112:1247–55. - PubMed
    1. McCormack VA, Menya D, Munishi MO, et al. Informing etiologic research priorities for squamous cell esophageal cancer in Africa: a review of setting-specific exposures to known and putative risk factors. Int J Cancer 2017;140:259–71. - PMC - PubMed
    1. Toh Y, Oki E, Ohgaki K, et al. Alcohol drinking, cigarette smoking, and the development of squamous cell carcinoma of the esophagus: molecular mechanisms of carcinogenesis. Int J Clin Oncol 2010;15:135–44. - PubMed